Coordination of project stakeholders is critical to timely and consistent software delivery. In this short paper we present the problem of private information as a guiding framework or lens through which to interpret coordination dynamics within software organizations. We provide evidence of this problem in the form of specific challenges, collected via interviews from a diverse set of extended (i.e., non-development) stakeholders in a globally distributed software development organization.

We present an automatic fault localization technique which leverages time spectra as abstractions for program executions. Time spectra have been traditionally used for performance debugging. By contrast, we use them for functional correctness debugging by identifying pieces of program code that take a "suspicious" amount of time to execute. The approach can be summarized as follows: Time spectra are collected from passing and failing runs, observed behavior models are created using the time spectra collected from passing runs, and deviations from these models in failing runs are identified and scored as potential causes of failures. Our empirical evaluations conducted on three real-life projects suggest that the proposed approach can effectively reduce the space of potential root causes for failures, which can in turn improve the turn around time for fixes.

We present a minimalist approach to model-based test generation. Our approach leverages the information provided in the domain and behavioral models of an application under test to produce a small yet effective set of test cases without additional user input. The domain model consists of UML class diagram with invariants, while the behavioral model consists of UML use cases. Each use case flow has an associated guard condition and a set of updates (to the domain object diagram and the output parameters). We treat the model invariants to enable a novel specify once, test everywhere paradigm. Our approach frees the modeler from the responsibility of specifying appropriate alternate flows on use cases affected by each invariant - our analysis augments the specified use cases with appropriate alternate flows. Our approach then produces suitable testing goals which are refinements of the guard conditions on the augmented flows using a set of fault models. Another salient feature of our approach is generation of verification sequences to ensure that the object diagram updates associated with a given flow are implemented correctly. Our technique uses a novel set of fault models to mutate an object diagram and a novel algorithm which distinguishes between the original and the mutated object diagrams. We describe the techniques used in our test generation approach.

We present an automated approach to generate functional conformance tests for semantic Web services. The semantics of the Web services are defined using the inputs, outputs, preconditions, effects (IOPEs) paradigm. For each Web service, our approach produces testing goals which are refinements of the Web service preconditions using a set of fault models. A novel planner component accepts these testing goals, along with an initial state of the world and the Web service definitions to generate a sequence of Web service invocations as a test case. Another salient feature of our approach is generation of verification sequences to ensure that the changes to the world produced by an effect are implemented correctly. Lastly, a given application incorporating a set of semantic Web services may be accessible through several interfaces such as 1) direct invocation of the Web services, or 2) a graphical user interface (GUI). Our technique allows generation of executable test cases which can be applied through both interfaces. We describe the techniques used in our test generation approach. We also present results which compare two approaches: an existing manual approach without the formal IOPEs information and the IOPEs-based approach reported in this paper. These results indicate that the approach described here leads to substantial savings in effort with comparable results for requirements coverage and fault detection effectiveness.

Program debugging is a difficult and time-consuming task. Our ultimate goal in this work is to help developers reduce the space of potential root causes for failures, which can, in turn, improve the turn around time for bug fixes. We propose a novel and very different approach. Rather then focusing on how a program behaves by analyzing its source code and/or execution traces, we concentrate on how it should behave with respect to a given behavioral model. We identify and verify slices of the behavior model, that, once implemented wrong in the program, can potentially lead to failures. Not only do we identify functional differences between the programand its model, but we also provide a ranked list of diagnoses which might explain (or be associated with) these differences. Our experiments suggest that the proposed approach can be quite effective in reducing the search space for potential root causes for failures

Multi-panel systems are systems that interact with a user via multiple input panels. The flow through the panels is influenced by the interaction. Multi-panel systems are ubiquitous, and include panel-based legacy applications, automated teller machines, and Web-based systems. Finding regression test suites which efficiently cover the functionality of these systems is difficult, because it requires covering interactions between input fields within a panel as well as flows between panels. Previous approaches to covering input field interactions include partitioning methods and combinatorial design methods. State machines and decision tables have been used to cover flow between panels. None of these techniques produce a conceptually simple, unified model that supports both intra- and inter-panel coverage. Our new method for capturing and representing test specifications provides such a model. This model is then used to generate a locally minimal set of test cases which completely covers the model. We applied this technique in a pilot study for regression testing, and this pilot had promising results which we discuss. We conclude by presenting our plans for generalizing the technique beyond multi-panel systems and regression testing.